




'''simple docstring'''


import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch


def     SCREAMING_SNAKE_CASE							(   a_						:				Union[str, Any]      ):
  __a								=       os.path.join(args.tf_model_dir					,       'parameters.json'      )
  __a								=       json.loads(open(a_      ).read()      )
  if not params:
    raise ValueError(
        f"It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."      )
  if not args.output.endswith('.pt'      ):
    __a								=       args.output + '.pt'
  __a								=       OrderedDict()
  with tf.device('/CPU:0'      ):
    __a								=       tf.train.load_checkpoint(args.tf_model_dir      )
    __a								=       reader.get_variable_to_shape_map()
    for key_name in shapes.keys():
      __a								=       reader.get_tensor(a_      ).astype(np.floataa      )
      if key_name.endswith('/adam_m'      ) or key_name.endswith('/adam_v'      ):
        continue
      if key_name.startswith('pasts/'      ):
        if key_name.startswith('pasts/mlp'      ):
          __a								=       int(key_name[9]      )
        elif key_name.startswith('pasts/out'      ):
          __a								=       8
        __a								=       'model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
        __a								=       vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
        __a								=       torch.tensor(a_      )
      elif key_name.startswith('model/moe'      ):
        __a								=       int(key_name[9:].split('/'      )[0]      )
        if key_name.endswith('/switch_gating/kernel'      ):
          __a								=       'model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
          __a								=       vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
          __a								=       torch.tensor(a_      )
        elif key_name.endswith('/softmlp/kernel'      ):
          __a								=       'model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
          __a								=       vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
          __a								=       torch.tensor(a_      )
        elif key_name.endswith('/wo/kernel'      ) or key_name.endswith('/wi/kernel'      ):
          __a								=       key_name[-9:-7]
          for i in range(16      ):
            __a								=       'model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
            __a								=       (
                vnp[i].transpose([1, 0]      ).copy()
            )  # In Mesh-Tensorflow, it is one array, so it is divided
            __a								=       torch.tensor(a_      )
      elif key_name.startswith('model/mlp'      ):
        __a								=       int(key_name[9:].split('/'      )[0]      )
        if key_name.endswith('/p1/kernel'      ):
          __a								=       'model.blocks.%d.feed_forward.mlp.wi.weight' % player
          __a								=       vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
          __a								=       torch.tensor(a_      )
        elif key_name.endswith('/p1/bias'      ):
          __a								=       'model.blocks.%d.feed_forward.mlp.wi.bias' % player
          __a								=       vnp.copy()  # same because it is one dimensional
          __a								=       torch.tensor(a_      )
        elif key_name.endswith('/p2/kernel'      ):
          __a								=       'model.blocks.%d.feed_forward.mlp.wo.weight' % player
          __a								=       vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
          __a								=       torch.tensor(a_      )
        elif key_name.endswith('/p2/bias'      ):
          __a								=       'model.blocks.%d.feed_forward.mlp.wo.bias' % player
          __a								=       vnp.copy()  # same because it is one dimensional
          __a								=       torch.tensor(a_      )
      elif key_name.startswith('model/ln'      ):
        __a								=       int(key_name[8:].split('/'      )[0]      )
        if key_name.endswith('/b'      ):
          __a								=       'model.blocks.%d.feed_forward.norm.bias' % player
          __a								=       vnp.copy()  # same because it is one dimensional
          __a								=       torch.tensor(a_      )
        elif key_name.endswith('/g'      ):
          __a								=       'model.blocks.%d.feed_forward.norm.weight' % player
          __a								=       vnp.copy()  # same because it is one dimensional
          __a								=       torch.tensor(a_      )
      elif key_name.startswith('model/att'      ):
        __a								=       int(key_name[9:].split('/'      )[0]      )
        if key_name.endswith('/qkv/kernel'      ):
          __a								=       vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
          __a								=       state[:, 0, :, :]
          __a								=       state[:, 1, :, :]
          __a								=       state[:, 2, :, :]
          __a								=       (
              state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]      )
              .transpose([1, 0]      )
              .copy()
          )  # Mesh-Tensorflow is a diagonal matrix
          __a								=       (
              state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]      )
              .transpose([1, 0]      )
              .copy()
          )  # Mesh-Tensorflow is a diagonal matrix
          __a								=       (
              state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]      )
              .transpose([1, 0]      )
              .copy()
          )  # Mesh-Tensorflow is a diagonal matrix
          __a								=       'model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
          __a								=       torch.tensor(a_      )
          __a								=       'model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
          __a								=       torch.tensor(a_      )
          __a								=       'model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
          __a								=       torch.tensor(a_      )
        elif key_name.endswith('/o/kernel'      ):
          __a								=       'model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
          __a								=       (
              vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]      ).transpose([1, 0]      ).copy()
          )  # Mesh-Tensorflow is a diagonal matrix
          __a								=       torch.tensor(a_      )
      elif key_name.startswith('model/an'      ):
        __a								=       int(key_name[8:].split('/'      )[0]      )
        if key_name.endswith('/b'      ):
          __a								=       'model.blocks.%d.self_attn.norm.bias' % player
          __a								=       vnp.copy()  # same because it is one dimensional
          __a								=       torch.tensor(a_      )
        elif key_name.endswith('/g'      ):
          __a								=       'model.blocks.%d.self_attn.norm.weight' % player
          __a								=       vnp.copy()  # same because it is one dimensional
          __a								=       torch.tensor(a_      )
      elif (
          key_name.startswith('model/wte'      )
          or key_name.startswith('model/wpe'      )
          or key_name.startswith('model/ete'      )
      ):
        __a								=       {'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
            key_name[-3:]
        ]
        __a								=       'model.%s.weight' % nlayer
        __a								=       vnp.copy()  # same in embedded
        __a								=       torch.tensor(a_      )
        if key_name.startswith('model/wte'      ):
          __a								=       'lm_head.weight'
          __a								=       vnp.copy()  # same in embedded
          __a								=       torch.tensor(a_      )
      elif key_name.startswith('model/wob'      ):
        __a								=       'final_logits_bias'
        __a								=       vnp.copy()  # same in embedded
        __a								=       state.reshape((1, -1)      )
        __a								=       torch.tensor(a_      )
      elif key_name == "model/dense/kernel":
        __a								=       'model.last_project.weight'
        __a								=       vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
        __a								=       torch.tensor(a_      )
      elif key_name == "model/dense_1/bias":
        __a								=       'model.last_project.bias'
        __a								=       vnp.copy()  # same because it is one dimensional
        __a								=       torch.tensor(a_      )
  torch.save(a_					,       args.output      )


if __name__ == "__main__":
  UpperCAmelCase_					=   argparse.ArgumentParser(
      description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
  )
  parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
  parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
  UpperCAmelCase_					=   parser.parse_args()
  convert_tf_gptsan_to_pt(args)









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_					=   {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   ["FNetTokenizer"]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   ["FNetTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "FNetForMaskedLM",
      "FNetForMultipleChoice",
      "FNetForNextSentencePrediction",
      "FNetForPreTraining",
      "FNetForQuestionAnswering",
      "FNetForSequenceClassification",
      "FNetForTokenClassification",
      "FNetLayer",
      "FNetModel",
      "FNetPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet import FNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet_fast import FNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_fnet import (
        FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        FNetForMaskedLM,
        FNetForMultipleChoice,
        FNetForNextSentencePrediction,
        FNetForPreTraining,
        FNetForQuestionAnswering,
        FNetForSequenceClassification,
        FNetForTokenClassification,
        FNetLayer,
        FNetModel,
        FNetPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



